----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  c:\expats\partisanship.invitation\Mex.imm.2006.panel.log
  log type:  text
 opened on:   2 Mar 2016, 12:05:24

. set matsize 150;

. set memory 70m;
set memory ignored.
    Memory no longer needs to be set in modern Statas; memory adjustments are performed on the fly automatically.

. ** Version 13 of STATA used for these analyses;
. ** This file replicates the analyses presented for the 2006 campaign panel study;
. use c:\expats\partisanship.invitation\Mex.imm.2006.panel.dta;

. ** The following statements produce the frequency distributions for Mexican immigrants in Table 1;
. tab uspid06a;

    Party ID, |
    June wave |      Freq.     Percent        Cum.
--------------+-----------------------------------
          dem |        107       14.21       14.21
          rep |         66        8.76       22.97
other/dk/none |        580       77.03      100.00
--------------+-----------------------------------
        Total |        753      100.00

. tab uspid06b;

Party ID, Nov |
         wave |      Freq.     Percent        Cum.
--------------+-----------------------------------
          dem |         82       31.06       31.06
          rep |         16        6.06       37.12
other/dk/none |        166       62.88      100.00
--------------+-----------------------------------
        Total |        264      100.00

. ** Note that instructions to reproduce the distributions in the lower half of Table 1 are given in the footnote to this table;
. ** The Pew dataset is publicly archived at: http://www.pewhispanic.org/2006/07/13/2006-national-survey-of-latinos/ ;
. ** The CCES dataset is publicly archived at: http://projects.iq.harvard.edu/cces/home ;
. ** the Stuart-Maxwell statistic to evaluate changes in categorical distributions for paired samples, mentioned in a footnote, is calculated through this command;
. symmetry uspid06a uspid06b;

--------------------------------------------------------------------------
Party ID,     |                     Party ID, Nov wave                    
June wave     |      dem            rep       other/dk/none      Total    
--------------+-----------------------------------------------------------
          dem |       30              3             14             47     
          rep |        8              3             11             22     
other/dk/none |       44             10            141            195     
              | 
        Total |       82             16            166            264     
--------------------------------------------------------------------------

                                             chi2     df      Prob>chi2
------------------------------------------------------------------------
Symmetry (asymptotic)                 |     17.84      3         0.0005
Marginal homogeneity (Stuart-Maxwell) |     17.76      2         0.0001
------------------------------------------------------------------------

. ** this command generates the table for reproducing the findings for Mexican immigrants in Table S1;
. tabulate uspid06a uspid06b;

    Party ID, |        Party ID, Nov wave
    June wave |       dem        rep  other/dk/ |     Total
--------------+---------------------------------+----------
          dem |        30          3         14 |        47 
          rep |         8          3         11 |        22 
other/dk/none |        44         10        141 |       195 
--------------+---------------------------------+----------
        Total |        82         16        166 |       264 


. ** Note that the ANES panel study referenced in Table S1 is publicly available at: http://www.electionstudies.org/studypages/2008_2009panel/anes2008_2009panel.htm ;
. ** the commands below replicate Table 2;
. mi set mlong;

. gen papers=civicstatus;
(102 missing values generated)

. gen undoc=civicstatus;
(102 missing values generated)

. recode papers 2=1 1=0 3=0;
(papers: 651 changes made)

. recode undoc 3=1 2=0 1=0;
(undoc: 651 changes made)

. gen citizen=civicstatus;
(102 missing values generated)

. recode citizen 1=1 2=0 3=0;
(citizen: 602 changes made)

. gen indiana=market;

. gen dallas=market;

. recode indiana 3=1 2=0 1=0;
(indiana: 753 changes made)

. recode dallas 1=1 2=0 3=0;
(dallas: 403 changes made)

. gen sandiego=market;

. recode sandiego 2=1 1=0 3=0;
(sandiego: 753 changes made)

. gen demid1=uspid06a;

. gen repid1=uspid06a;

. recode demid1 1=1 2=0 3=0;
(demid1: 646 changes made)

. recode repid1 2=1 1=0 3=0;
(repid1: 753 changes made)

. gen havepid2=uspid06b;
(489 missing values generated)

. recode havepid2 1=1 2=1 3=0;
(havepid2: 182 changes made)

. gen havepid1=uspid06a;

. recode havepid1 1=1 2=1 3=0;
(havepid1: 646 changes made)

. gen likedem1=ptyeval06a;

. gen likerep1=ptyeval06a;

. recode likedem1 1=1 2=0 3=0;
(likedem1: 580 changes made)

. recode likerep1 2=1 1=0 3=0;
(likerep1: 753 changes made)

. gen likepty1=likedem1+likerep1;

. gen likepty2=ptyeval06b;
(489 missing values generated)

. recode likepty2 1=1 2=1 3=0;
(likepty2: 152 changes made)

. mi register imputed uspid06b ptyeval06b havepid2 yearsus1 uspolint1 school1 age1 relig1 usespan1 ffmex1 remit1 civicstatus;
(538 m=0 obs. now marked as incomplete)

. mi impute chained (mlogit) civicstatus (logit) havepid2 (regress) uspolint1 yearsus1 school1 age1 relig1 ffmex1 
> remit1 usespan1 = female1 i.havepid1 i.likepty1 i.market affluen1, add(20) rseed(902);

Conditional models:
            relig1: regress relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
         uspolint1: regress uspolint1 relig1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
           school1: regress school1 relig1 uspolint1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
          usespan1: regress usespan1 relig1 uspolint1 school1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
            ffmex1: regress ffmex1 relig1 uspolint1 school1 usespan1 age1 remit1 i.civicstatus yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
              age1: regress age1 relig1 uspolint1 school1 usespan1 ffmex1 remit1 i.civicstatus yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
            remit1: regress remit1 relig1 uspolint1 school1 usespan1 ffmex1 age1 i.civicstatus yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
       civicstatus: mlogit civicstatus relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 yearsus1 i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
          yearsus1: regress yearsus1 relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus i.havepid2 female1 i.havepid1 i.likepty1 i.market affluen1
          havepid2: logit havepid2 relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 female1 i.havepid1 i.likepty1 i.market affluen1

Performing chained iterations ...

Multivariate imputation                     Imputations =       20
Chained equations                                 added =       20
Imputed: m=1 through m=20                       updated =        0

Initialization: monotone                     Iterations =      200
                                                burn-in =       10

       civicstatus: multinomial logistic regression
          havepid2: logistic regression
         uspolint1: linear regression
          yearsus1: linear regression
           school1: linear regression
              age1: linear regression
            relig1: linear regression
            ffmex1: linear regression
            remit1: linear regression
          usespan1: linear regression

------------------------------------------------------------------
                   |               Observations per m             
                   |----------------------------------------------
          Variable |   Complete   Incomplete   Imputed |     Total
-------------------+-----------------------------------+----------
       civicstatus |        651          102       102 |       753
          havepid2 |        264          489       489 |       753
         uspolint1 |        745            8         8 |       753
          yearsus1 |        640          113       113 |       753
           school1 |        743           10        10 |       753
              age1 |        738           15        15 |       753
            relig1 |        749            4         4 |       753
            ffmex1 |        741           12        12 |       753
            remit1 |        738           15        15 |       753
          usespan1 |        742           11        11 |       753
------------------------------------------------------------------
(complete + incomplete = total; imputed is the minimum across m
 of the number of filled-in observations.)

. mi estimate: logit havepid2 havepid1 ;

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =        753
                                                  Average RVI     =     2.1163
                                                  Largest FMI     =     0.7210
DF adjustment:   Large sample                     DF:     min     =      38.04
                                                          avg     =      42.78
                                                          max     =      47.52
Model F test:       Equal FMI                     F(   1,   47.5) =      25.09
Within VCE type:          OIM                     Prob > F        =     0.0000

------------------------------------------------------------------------------
    havepid2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    havepid1 |   1.518401   .3031517     5.01   0.000     .9087149    2.128086
       _cons |  -1.003466   .1735818    -5.78   0.000    -1.354853   -.6520799
------------------------------------------------------------------------------

. mi estimate, or: logit havepid2 havepid1 ;

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =        753
                                                  Average RVI     =     2.1163
                                                  Largest FMI     =     0.7210
DF adjustment:   Large sample                     DF:     min     =      38.04
                                                          avg     =      42.78
                                                          max     =      47.52
Model F test:       Equal FMI                     F(   1,   47.5) =      25.09
Within VCE type:          OIM                     Prob > F        =     0.0000

------------------------------------------------------------------------------
    havepid2 | Odds Ratio   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    havepid1 |   4.564918   1.383863     5.01   0.000     2.481132     8.39878
       _cons |   .3666064   .0636362    -5.78   0.000     .2579852    .5209611
------------------------------------------------------------------------------

. mi estimate: logit havepid2 i.havepid1 i.market yearsus1 i.civicstatus uspolint1 school1 age1 female1 affluen1 usespan1 relig1 ffmex1 remit1;

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =        753
                                                  Average RVI     =     2.3188
                                                  Largest FMI     =     0.8321
DF adjustment:   Large sample                     DF:     min     =      28.22
                                                          avg     =      43.76
                                                          max     =      71.09
Model F test:       Equal FMI                     F(  15,  580.3) =       1.77
Within VCE type:          OIM                     Prob > F        =     0.0351

----------------------------------------------------------------------------------------
              havepid2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
            1.havepid1 |   1.379146   .3324904     4.15   0.000     .7102809    2.048012
                       |
                market |
            San Diego  |  -.2790139   .4190863    -0.67   0.509     -1.12188    .5638527
              Indiana  |   .0297538   .2728914     0.11   0.913    -.5143647    .5738724
                       |
              yearsus1 |   .0124029   .0231322     0.54   0.595    -.0342398    .0590457
                       |
           civicstatus |
   noncitizen, papers  |  -.5230191   .6426674    -0.81   0.420    -1.820154    .7741155
noncitizen, no papers  |  -.6903785   .6745892    -1.02   0.312    -2.051562    .6708051
                       |
             uspolint1 |    .209244   .1772055     1.18   0.247    -.1519584    .5704464
               school1 |   .1137543    .087224     1.30   0.200    -.0627447    .2902533
                  age1 |  -.0023205    .016676    -0.14   0.890    -.0359959    .0313549
               female1 |  -.4074974   .3271431    -1.25   0.220    -1.069526    .2545317
              affluen1 |  -.0345821   .2599737    -0.13   0.895      -.56693    .4977657
              usespan1 |   .2074969   .2785487     0.74   0.459    -.3484648    .7634587
                relig1 |   .0474068   .1276068     0.37   0.712    -.2092578    .3040714
                ffmex1 |   .1453562   .1694591     0.86   0.396    -.1963071    .4870196
                remit1 |  -.0221215   .1259878    -0.18   0.862    -.2773343    .2330913
                 _cons |  -2.252582   1.826619    -1.23   0.226    -5.963168    1.458004
----------------------------------------------------------------------------------------

. mi estimate, or: logit havepid2 i.havepid1 i.market yearsus1 i.civicstatus uspolint1 school1 age1 female1 affluen1 usespan1 relig1 ffmex1 remit1;

Multiple-imputation estimates                     Imputations     =         20
Logistic regression                               Number of obs   =        753
                                                  Average RVI     =     2.3188
                                                  Largest FMI     =     0.8321
DF adjustment:   Large sample                     DF:     min     =      28.22
                                                          avg     =      43.76
                                                          max     =      71.09
Model F test:       Equal FMI                     F(  15,  580.3) =       1.77
Within VCE type:          OIM                     Prob > F        =     0.0351

----------------------------------------------------------------------------------------
              havepid2 | Odds Ratio   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
            1.havepid1 |   3.971509   1.320489     4.15   0.000     2.034563    7.752471
                       |
                market |
            San Diego  |   .7565294   .3170511    -0.67   0.509     .3256668     1.75743
              Indiana  |   1.030201    .281133     0.11   0.913     .5978803    1.775128
                       |
              yearsus1 |    1.01248   .0234209     0.54   0.595     .9663397    1.060824
                       |
           civicstatus |
   noncitizen, papers  |   .5927284   .3809272    -0.81   0.420     .1620009    2.168673
noncitizen, no papers  |   .5013863   .3382298    -1.02   0.312      .128534    1.955811
                       |
             uspolint1 |   1.232746   .2184493     1.18   0.247      .859024    1.769057
               school1 |   1.120477   .0977325     1.30   0.200     .9391832    1.336766
                  age1 |   .9976822   .0166374    -0.14   0.890     .9646442    1.031852
               female1 |   .6653132   .2176526    -1.25   0.220      .343171    1.289857
              affluen1 |    .966009    .251137    -0.13   0.895     .5672643    1.645042
              usespan1 |   1.230594   .3427803     0.74   0.459     .7057707    2.145685
                relig1 |   1.048548   .1338019     0.37   0.712     .8111861    1.355366
                ffmex1 |   1.156451   .1959712     0.86   0.396     .8217598    1.627459
                remit1 |   .9781214   .1232314    -0.18   0.862     .7578011    1.262497
                 _cons |   .1051274   .1920278    -1.23   0.226     .0025718    4.297374
----------------------------------------------------------------------------------------

. ** these post-estimation commands are used to test the joint significance of multiple-category indicator variables;
. mi test 2.market ;
note: assuming equal fractions of missing information

 ( 1)  [havepid2]2.market = 0

       F(  1,  47.5) =    0.44
            Prob > F =    0.5088

. mi test 3.market;
note: assuming equal fractions of missing information

 ( 1)  [havepid2]3.market = 0

       F(  1,  71.1) =    0.01
            Prob > F =    0.9135

. mi test 3.market 2.market;
note: assuming equal fractions of missing information

 ( 1)  [havepid2]3.market = 0
 ( 2)  [havepid2]2.market = 0

       F(  2, 100.3) =    0.31
            Prob > F =    0.7365

. mi test 2.civicstatus;
note: assuming equal fractions of missing information

 ( 1)  [havepid2]2.civicstatus = 0

       F(  1,  41.8) =    0.66
            Prob > F =    0.4204

. mi test 3.civicstatus;
note: assuming equal fractions of missing information

 ( 1)  [havepid2]3.civicstatus = 0

       F(  1,  42.2) =    1.05
            Prob > F =    0.3119

. mi test 2.civicstatus 3.civicstatus;
note: assuming equal fractions of missing information

 ( 1)  [havepid2]2.civicstatus = 0
 ( 2)  [havepid2]3.civicstatus = 0

       F(  2,  68.9) =    0.46
            Prob > F =    0.6323

. clear;

. ** these commands replicate the MNL model in Table S2;
. use c:\expats\partisanship.invitation\Mex.imm.2006.panel.dta;

. mi set mlong;

. gen likedem1=ptyeval06a;

. gen likerep1=ptyeval06a;

. recode likedem1 1=1 2=0 3=0;
(likedem1: 580 changes made)

. recode likerep1 2=1 1=0 3=0;
(likerep1: 753 changes made)

. gen papers=civicstatus;
(102 missing values generated)

. gen undoc=civicstatus;
(102 missing values generated)

. recode papers 2=1 1=0 3=0;
(papers: 651 changes made)

. recode undoc 3=1 2=0 1=0;
(undoc: 651 changes made)

. gen citizen=civicstatus;
(102 missing values generated)

. recode citizen 1=1 2=0 3=0;
(citizen: 602 changes made)

. gen indiana=market;

. gen dallas=market;

. recode indiana 3=1 2=0 1=0;
(indiana: 753 changes made)

. recode dallas 1=1 2=0 3=0;
(dallas: 403 changes made)

. gen sandiego=market;

. recode sandiego 2=1 1=0 3=0;
(sandiego: 753 changes made)

. gen demid1=uspid06a;

. gen repid1=uspid06a;

. recode demid1 1=1 2=0 3=0;
(demid1: 646 changes made)

. recode repid1 2=1 1=0 3=0;
(repid1: 753 changes made)

. gen likepty1=likedem1+likerep1;

. gen havepid1=demid1+repid1;

. gen likepty2=ptyeval06b;
(489 missing values generated)

. recode likepty2 1=1 2=1 3=0;
(likepty2: 152 changes made)

. gen havepid2=uspid06b;
(489 missing values generated)

. recode havepid2 1=1 2=1 3=0;
(havepid2: 182 changes made)

. mi register imputed ptyeval06b uspid06b yearsus1 uspolint1 school1 age1 relig1 usespan1 ffmex1 remit1 civicstatus;
(538 m=0 obs. now marked as incomplete)

. mi impute chained (mlogit) civicstatus uspid06b (regress) uspolint1 yearsus1 school1 age1 relig1 ffmex1 
> remit1 usespan1 = female1 i.uspid06a i.ptyeval06a i.market affluen, add(20) rseed(902);

Conditional models:
            relig1: regress relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
         uspolint1: regress uspolint1 relig1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
           school1: regress school1 relig1 uspolint1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
          usespan1: regress usespan1 relig1 uspolint1 school1 ffmex1 age1 remit1 i.civicstatus yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
            ffmex1: regress ffmex1 relig1 uspolint1 school1 usespan1 age1 remit1 i.civicstatus yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
              age1: regress age1 relig1 uspolint1 school1 usespan1 ffmex1 remit1 i.civicstatus yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
            remit1: regress remit1 relig1 uspolint1 school1 usespan1 ffmex1 age1 i.civicstatus yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
       civicstatus: mlogit civicstatus relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 yearsus1 i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
          yearsus1: regress yearsus1 relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus i.uspid06b female1 i.uspid06a i.ptyeval06a i.market affluen1
          uspid06b: mlogit uspid06b relig1 uspolint1 school1 usespan1 ffmex1 age1 remit1 i.civicstatus yearsus1 female1 i.uspid06a i.ptyeval06a i.market affluen1

Performing chained iterations ...

Multivariate imputation                     Imputations =       20
Chained equations                                 added =       20
Imputed: m=1 through m=20                       updated =        0

Initialization: monotone                     Iterations =      200
                                                burn-in =       10

       civicstatus: multinomial logistic regression
          uspid06b: multinomial logistic regression
         uspolint1: linear regression
          yearsus1: linear regression
           school1: linear regression
              age1: linear regression
            relig1: linear regression
            ffmex1: linear regression
            remit1: linear regression
          usespan1: linear regression

------------------------------------------------------------------
                   |               Observations per m             
                   |----------------------------------------------
          Variable |   Complete   Incomplete   Imputed |     Total
-------------------+-----------------------------------+----------
       civicstatus |        651          102       102 |       753
          uspid06b |        264          489       489 |       753
         uspolint1 |        745            8         8 |       753
          yearsus1 |        640          113       113 |       753
           school1 |        743           10        10 |       753
              age1 |        738           15        15 |       753
            relig1 |        749            4         4 |       753
            ffmex1 |        741           12        12 |       753
            remit1 |        738           15        15 |       753
          usespan1 |        742           11        11 |       753
------------------------------------------------------------------
(complete + incomplete = total; imputed is the minimum across m
 of the number of filled-in observations.)

. mi estimate: mlogit uspid06b ib3.uspid06a i.market yearsus1 i.civicstatus uspolint1 school1 age1 female1 affluen usespan1 relig ffmex remit, base(3);

Multiple-imputation estimates                     Imputations     =         20
Multinomial logistic regression                   Number of obs   =        753
                                                  Average RVI     =     2.2392
                                                  Largest FMI     =     0.8840
DF adjustment:   Large sample                     DF:     min     =      24.82
                                                          avg     =      44.90
                                                          max     =      85.05
Model F test:       Equal FMI                     F(  32, 1250.2) =       1.33
Within VCE type:          OIM                     Prob > F        =     0.1036

----------------------------------------------------------------------------------------
              uspid06b |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
dem                    |
              uspid06a |
                  dem  |   1.766661   .3762146     4.70   0.000     1.017027    2.516296
                  rep  |   .9292793   .4446654     2.09   0.040     .0426305    1.815928
                       |
                market |
            San Diego  |  -.2182572   .3720115    -0.59   0.559      -.95791    .5213957
              Indiana  |  -.0464856   .3591056    -0.13   0.898    -.7704608    .6774896
                       |
              yearsus1 |   .0109202   .0264161     0.41   0.681    -.0423892    .0642296
                       |
           civicstatus |
   noncitizen, papers  |  -.1917539   .6117812    -0.31   0.755    -1.415604    1.032096
noncitizen, no papers  |  -.3824312   .6618266    -0.58   0.566     -1.70819    .9433271
                       |
             uspolint1 |   .1885955   .1536216     1.23   0.226    -.1207124    .4979035
               school1 |   .1825663   .0768678     2.38   0.021      .029031    .3361017
                  age1 |   .0075875   .0165269     0.46   0.648    -.0256276    .0408026
               female1 |  -.4142299   .3295705    -1.26   0.215    -1.078088    .2496286
              affluen1 |  -.0429353   .2254569    -0.19   0.850    -.4994131    .4135425
              usespan1 |   .4164267   .3724126     1.12   0.270    -.3345604    1.167414
                relig1 |   .0123696    .150926     0.08   0.935    -.2926576    .3173967
                ffmex1 |   .0405752   .1591813     0.25   0.800    -.2776039    .3587543
                remit1 |   .0058042   .1227124     0.05   0.962     -.241131    .2527393
                 _cons |  -3.550349   1.566828    -2.27   0.027    -6.684924   -.4157727
-----------------------+----------------------------------------------------------------
rep                    |
              uspid06a |
                  dem  |   1.223721   .8068046     1.52   0.136    -.3977307    2.845173
                  rep  |   1.302894    .704119     1.85   0.069    -.1021281    2.707915
                       |
                market |
            San Diego  |  -1.365257   1.413711    -0.97   0.341    -4.236252    1.505737
              Indiana  |   .8592913   .7469332     1.15   0.259    -.6628546    2.381437
                       |
              yearsus1 |   .0705985   .0546648     1.29   0.206    -.0408423    .1820392
                       |
           civicstatus |
   noncitizen, papers  |  -1.536383   1.278587    -1.20   0.238    -4.136226    1.063459
noncitizen, no papers  |  -1.210201   1.234875    -0.98   0.334    -3.715036    1.294634
                       |
             uspolint1 |   .3831855   .3311485     1.16   0.256    -.2913393     1.05771
               school1 |  -.3002064   .3022866    -0.99   0.330    -.9230087    .3225959
                  age1 |  -.0663178   .0380234    -1.74   0.089    -.1431891    .0105534
               female1 |  -.4295672   .7199453    -0.60   0.555    -1.896437    1.037302
              affluen1 |   -.249554    .556409    -0.45   0.658    -1.393753    .8946449
              usespan1 |  -.4883742   .6084484    -0.80   0.427    -1.720507    .7437582
                relig1 |   .2477427   .3219704     0.77   0.447     -.408777    .9042625
                ffmex1 |   .8548063   .4445076     1.92   0.062    -.0465731    1.756186
                remit1 |   .0867433   .2902674     0.30   0.767    -.5060049    .6794915
                 _cons |  -1.829598   4.039358    -0.45   0.654    -10.08407    6.424872
-----------------------+----------------------------------------------------------------
other_dk_none          |  (base outcome)
----------------------------------------------------------------------------------------

. mi estimate, rrr: mlogit uspid06b ib3.uspid06a i.market yearsus1 i.civicstatus uspolint1 school1 age1 female1 affluen usespan1 relig ffmex remit, base(3);

Multiple-imputation estimates                     Imputations     =         20
Multinomial logistic regression                   Number of obs   =        753
                                                  Average RVI     =     2.2392
                                                  Largest FMI     =     0.8840
DF adjustment:   Large sample                     DF:     min     =      24.82
                                                          avg     =      44.90
                                                          max     =      85.05
Model F test:       Equal FMI                     F(  32, 1250.2) =       1.33
Within VCE type:          OIM                     Prob > F        =     0.1036

----------------------------------------------------------------------------------------
              uspid06b |        RRR   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
dem                    |
              uspid06a |
                  dem  |   5.851285   2.201339     4.70   0.000     2.764961    12.38264
                  rep  |   2.532683   1.126197     2.09   0.040     1.043552    6.146779
                       |
                market |
            San Diego  |   .8039187    .299067    -0.59   0.559      .383694    1.684377
              Indiana  |   .9545783   .3427944    -0.13   0.898     .4627998    1.968929
                       |
              yearsus1 |    1.01098   .0267061     0.41   0.681     .9584967    1.066337
                       |
           civicstatus |
   noncitizen, papers  |     .82551   .5050315    -0.31   0.755      .242779    2.806944
noncitizen, no papers  |   .6822008   .4514987    -0.58   0.566     .1811935    2.568513
                       |
             uspolint1 |   1.207552   .1855061     1.23   0.226     .8862888    1.645268
               school1 |   1.200294    .092264     2.38   0.021     1.029457    1.399481
                  age1 |   1.007616   .0166528     0.46   0.648      .974698    1.041646
               female1 |    .660849   .2177964    -1.26   0.215     .3402453    1.283549
              affluen1 |   .9579734   .2159817    -0.19   0.850     .6068868    1.512165
              usespan1 |   1.516533   .5647759     1.12   0.270     .7156526    3.213671
                relig1 |   1.012446   .1528044     0.08   0.935     .7462776    1.373547
                ffmex1 |    1.04141   .1657729     0.25   0.800     .7575968    1.431545
                remit1 |   1.005821   .1234267     0.05   0.962     .7857387    1.287548
                 _cons |   .0287146   .0449909    -2.27   0.027     .0012496    .6598302
-----------------------+----------------------------------------------------------------
rep                    |
              uspid06a |
                  dem  |   3.399816   2.742987     1.52   0.136     .6718429    17.20454
                  rep  |   3.679929   2.591108     1.85   0.069     .9029139    14.99797
                       |
                market |
            San Diego  |   .2553149   .3609416    -0.97   0.341     .0144617    4.507473
              Indiana  |   2.361487   1.763873     1.15   0.259     .5153781    10.82044
                       |
              yearsus1 |    1.07315   .0586636     1.29   0.206     .9599805    1.199661
                       |
           civicstatus |
   noncitizen, papers  |   .2151579   .2750979    -1.20   0.238     .0159831    2.896372
noncitizen, no papers  |   .2981374   .3681625    -0.98   0.334     .0243546     3.64966
                       |
             uspolint1 |    1.46695   .4857783     1.16   0.256     .7472621     2.87977
               school1 |   .7406653   .2238932    -0.99   0.330     .3973218    1.380707
                  age1 |   .9358334   .0355836    -1.74   0.089     .8665902    1.010609
               female1 |   .6507907   .4685337    -0.60   0.555     .1501025    2.821595
              affluen1 |   .7791482    .433525    -0.45   0.658     .2481423    2.446467
              usespan1 |   .6136232   .3733581    -0.80   0.427     .1789755    2.103827
                relig1 |    1.28113   .4124861     0.77   0.447     .6644624     2.47011
                ffmex1 |   2.350919   1.045001     1.92   0.062     .9544948     5.79031
                remit1 |   1.090617   .3165704     0.30   0.767     .6028994    1.972874
                 _cons |    .160478    .648228    -0.45   0.654     .0000417    617.0019
-----------------------+----------------------------------------------------------------
other_dk_none          |  (base outcome)
----------------------------------------------------------------------------------------

. mi test 1.uspid06a;
note: assuming equal fractions of missing information

 ( 1)  [dem]1.uspid06a = 0
 ( 2)  [rep]1.uspid06a = 0
 ( 3)  [other_dk_none]1o.uspid06a = 0
       Constraint 3 dropped

       F(  2,  98.6) =    9.30
            Prob > F =    0.0002

.  mi test 2.uspid06a;
note: assuming equal fractions of missing information

 ( 1)  [dem]2.uspid06a = 0
 ( 2)  [rep]2.uspid06a = 0
 ( 3)  [other_dk_none]2o.uspid06a = 0
       Constraint 3 dropped

       F(  2, 118.5) =    3.06
            Prob > F =    0.0505

. mi test 1.uspid06a 2.uspid06a;
note: assuming equal fractions of missing information

 ( 1)  [dem]1.uspid06a = 0
 ( 2)  [rep]1.uspid06a = 0
 ( 3)  [other_dk_none]1o.uspid06a = 0
 ( 4)  [dem]2.uspid06a = 0
 ( 5)  [rep]2.uspid06a = 0
 ( 6)  [other_dk_none]2o.uspid06a = 0
       Constraint 3 dropped
       Constraint 6 dropped

       F(  4, 235.0) =    5.92
            Prob > F =    0.0001

. mi test 2.market ;
note: assuming equal fractions of missing information

 ( 1)  [dem]2.market = 0
 ( 2)  [rep]2.market = 0
 ( 3)  [other_dk_none]2o.market = 0
       Constraint 3 dropped

       F(  2,  82.7) =    0.70
            Prob > F =    0.4999

. mi test 3.market;
note: assuming equal fractions of missing information

 ( 1)  [dem]3.market = 0
 ( 2)  [rep]3.market = 0
 ( 3)  [other_dk_none]3o.market = 0
       Constraint 3 dropped

       F(  2,  65.3) =    0.84
            Prob > F =    0.4344

. mi test 3.market 2.market;
note: assuming equal fractions of missing information

 ( 1)  [dem]3.market = 0
 ( 2)  [rep]3.market = 0
 ( 3)  [other_dk_none]3o.market = 0
 ( 4)  [dem]2.market = 0
 ( 5)  [rep]2.market = 0
 ( 6)  [other_dk_none]2o.market = 0
       Constraint 3 dropped
       Constraint 6 dropped

       F(  4, 158.9) =    1.04
            Prob > F =    0.3868

. mi test yearsus1;
note: assuming equal fractions of missing information

 ( 1)  [dem]yearsus1 = 0
 ( 2)  [rep]yearsus1 = 0
 ( 3)  [other_dk_none]o.yearsus1 = 0
       Constraint 3 dropped

       F(  2,  63.5) =    0.96
            Prob > F =    0.3884

. mi test 2.civicstatus;
note: assuming equal fractions of missing information

 ( 1)  [dem]2.civicstatus = 0
 ( 2)  [rep]2.civicstatus = 0
 ( 3)  [other_dk_none]2o.civicstatus = 0
       Constraint 3 dropped

       F(  2,  75.1) =    0.94
            Prob > F =    0.3943

. mi test 3.civicstatus;
note: assuming equal fractions of missing information

 ( 1)  [dem]3.civicstatus = 0
 ( 2)  [rep]3.civicstatus = 0
 ( 3)  [other_dk_none]3o.civicstatus = 0
       Constraint 3 dropped

       F(  2,  77.4) =    0.61
            Prob > F =    0.5459

. mi test 2.civicstatus 3.civicstatus;
note: assuming equal fractions of missing information

 ( 1)  [dem]2.civicstatus = 0
 ( 2)  [rep]2.civicstatus = 0
 ( 3)  [other_dk_none]2o.civicstatus = 0
 ( 4)  [dem]3.civicstatus = 0
 ( 5)  [rep]3.civicstatus = 0
 ( 6)  [other_dk_none]3o.civicstatus = 0
       Constraint 3 dropped
       Constraint 6 dropped

       F(  4, 174.4) =    0.60
            Prob > F =    0.6645

. mi test uspolint1;
note: assuming equal fractions of missing information

 ( 1)  [dem]uspolint1 = 0
 ( 2)  [rep]uspolint1 = 0
 ( 3)  [other_dk_none]o.uspolint1 = 0
       Constraint 3 dropped

       F(  2,  68.7) =    1.22
            Prob > F =    0.3013

. mi test school1;
note: assuming equal fractions of missing information

 ( 1)  [dem]school1 = 0
 ( 2)  [rep]school1 = 0
 ( 3)  [other_dk_none]o.school1 = 0
       Constraint 3 dropped

       F(  2,  56.2) =    2.43
            Prob > F =    0.0970

. mi test age1;
note: assuming equal fractions of missing information

 ( 1)  [dem]age1 = 0
 ( 2)  [rep]age1 = 0
 ( 3)  [other_dk_none]o.age1 = 0
       Constraint 3 dropped

       F(  2,  81.0) =    2.01
            Prob > F =    0.1403

. mi test female1;
note: assuming equal fractions of missing information

 ( 1)  [dem]female1 = 0
 ( 2)  [rep]female1 = 0
 ( 3)  [other_dk_none]o.female1 = 0
       Constraint 3 dropped

       F(  2,  67.1) =    0.74
            Prob > F =    0.4828

. mi test affluen1;
note: assuming equal fractions of missing information

 ( 1)  [dem]affluen1 = 0
 ( 2)  [rep]affluen1 = 0
 ( 3)  [other_dk_none]o.affluen1 = 0
       Constraint 3 dropped

       F(  2,  55.8) =    0.14
            Prob > F =    0.8670

. mi test usespan1;
note: assuming equal fractions of missing information

 ( 1)  [dem]usespan1 = 0
 ( 2)  [rep]usespan1 = 0
 ( 3)  [other_dk_none]o.usespan1 = 0
       Constraint 3 dropped

       F(  2,  77.2) =    1.23
            Prob > F =    0.2989

. mi test relig1;
note: assuming equal fractions of missing information

 ( 1)  [dem]relig1 = 0
 ( 2)  [rep]relig1 = 0
 ( 3)  [other_dk_none]o.relig1 = 0
       Constraint 3 dropped

       F(  2,  61.8) =    0.33
            Prob > F =    0.7197

. mi test ffmex1;
note: assuming equal fractions of missing information

 ( 1)  [dem]ffmex1 = 0
 ( 2)  [rep]ffmex1 = 0
 ( 3)  [other_dk_none]o.ffmex1 = 0
       Constraint 3 dropped

       F(  2,  78.3) =    2.26
            Prob > F =    0.1109

. mi test remit1;
note: assuming equal fractions of missing information

 ( 1)  [dem]remit1 = 0
 ( 2)  [rep]remit1 = 0
 ( 3)  [other_dk_none]o.remit1 = 0
       Constraint 3 dropped

       F(  2,  64.9) =    0.06
            Prob > F =    0.9442

. 
end of do-file

. exit, clear
